Garbage Collection is Fast, But a Stack is Faster

Prompted by claims that garbage collection can outperform stack allocation when sufficient physical memory is available, we present a careful analysis and set of cross-architecture measurements comparing these two approaches for the implementation of continuation (procedure call) frames. When the frames are allocated on a heap they require additional space, increase the amount of data transferred between memory and registers, and, on current architectures, require more instructions. We find that stack allocation of continuation frames outperforms heap allocation in some cases by almost a factor of three. Thus, stacks remain an important implementation technique for procedure calls, even in the presence of an efficient, compacting garbage collector and large amounts of memory.

Sparsely Faceted Arrays: A Mechanism Supporting Parallel Allocation, Communication, and Garbage Collection

Conventional parallel computer architectures do not provide support for non-uniformly distributed objects. In this thesis, I introduce sparsely faceted arrays (SFAs), a new low-level mechanism for naming regions of memory, or facets, on different processors in a distributed, shared memory parallel processing system. Sparsely faceted arrays address the disconnect between the global distributed arrays provided by conventional architectures (e.g. the Cray T3 series), and the requirements of high-level parallel programming methods that wish to use objects that are distributed over only a subset of processing elements. A sparsely faceted array names a virtual globally-distributed array, but actual facets are lazily allocated. By providing simple semantics and making efficient use of memory, SFAs enable efficient implementation of a variety of non-uniformly distributed data structures and related algorithms. I present example applications which use SFAs, and describe and evaluate simple hardware mechanisms for implementing SFAs. Keeping track of which nodes have allocated facets for a particular SFA is an important task that suggests the need for automatic memory management, including garbage collection. To address this need, I first argue that conventional tracing techniques such as mark/sweep and copying GC are inherently unscalable in parallel systems. I then present a parallel memory-management strategy, based on reference-counting, that is capable of garbage collecting sparsely faceted arrays. I also discuss opportunities for hardware support of this garbage collection strategy. I have implemented a high-level hardware/OS simulator featuring hardware support for sparsely faceted arrays and automatic garbage collection. I describe the simulator and outline a few of the numerous details associated with a "real" implementation of SFAs and SFA-aware garbage collection. Simulation results are used throughout this thesis in the evaluation of hardware support mechanisms.

Benefits of Implementing Lean Practices and the Impact of the Lean Aerospace Initiative in the Defense Aerospace Industry and Government Agencies

In the 1980’s, many United States industrial organizations started developing new production processes to improve quality, reduce cost, and better respond to customer needs and the pressures of global competition. This new paradigm was coined Lean Production (or simply “Lean”) in the book The Machine That Changed The World published in 1990 by researchers from MIT’s International Motor Vehicle Program. In 1993, a consortium of US defense aerospace firms and the USAF Aeronautical Systems Center, together with the AFRL Materials and Manufacturing Directorate, started the Lean Aircraft Initiative (LAI) at MIT. With expansion in 1998 to include government space products, the program was renamed the Lean Aerospace Initiative. LAI’s vision is to “Significantly reduce the cost and cycle time for military aerospace products throughout the entire value chain while continuing to improve product performance.” By late 1998, 23 industry and 13 government organizations with paying memberships, along with MIT and the UAW were participating in the LAI.